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Chicago

Altay, Bülent and Mert Topcu. "Re-Examining the Impact of Financial System on Economic Growth: New Evidence From Heterogeneous Regional Panels." In Handbook of Research on Global Enterprise Operations and Opportunities, ed. Mehdi Khosrow-Pour, 1-16 (2017), accessed February 22, 2018. doi:10.4018/978-1-5225-2245-4.ch001

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Abstract

Recent developments in panel data econometrics allow researchers to estimate heterogeneous parameters. Given this novelty, the goal of this paper is to revisit the financial development-economic growth nexus for a panel of 76 developing counties using recent heterogeneous panel time series estimation methods. Findings indicate that results are very volatile across different empirical specifications. Overall, results provide a strong support of a negative impact that banking development on growth. At regional level, however, there is relatively little evidence of such relationship. On the side of the stock market, there is no much indication in favor of stock market-led growth hypothesis either at pooled panel or at regional level.

Literature Review

The relationship between financial development and economic growth is not a new discovery. Bagehot (1873) identified the role of financial sector development on economic growth about 150 years ago. According to his study, the financial system plays a crucial role in stimulating industrialization in England by facilitating the mobilization of capital.

There are four different theoretical views about the relationship between financial development and economic growth. The first is supply-leading hypothesis, suggesting that the positive effects of financial development on economic growth, according to Schumpeter (1911). In this approach, causality runs from financial development to growth (see, for example: Roubini and Sala-i Martin, 1992; King and Levine, 1993a; b). Second, with the pioneer study of Robinson (1952), demand-following hypothesis states that the causality from economic growth to financial development (see, for example: Patrick, 1966; Jung, 1986; Ireland, 1994). The third approach, bi-directional causality hypothesis, emphasizes that there is a cause and effect relation between finance and growth (see, for example: Berthelemy and Varoudakis, 1996; Demetriades and Hussein, 1996; Blackburn and Hung, 1998). The fourth approach indicates that there is no causality between financial development and economic growth which is known as independent hypothesis (see, for example: Lucas, 1988; Stern, 1989; De Gregorio and Guidotti, 1995).

Key Terms in this Chapter

The Root Mean Square Errors (RMSE): The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used measure of the differences between values (sample and population values) predicted by a model or an estimator and the values actually observed.

Cointegration: Cointegration is a concept whereby time series have a fixed relationship in the long run.

Granger Causality: Granger causality is a statistical concept of causality that is based on prediction. According to Granger causality, if a signal X 1 “Granger-causes” (or “G-causes”) a signal X 2 , then past values of X 1 should contain information that helps predict X 2 above and beyond the information contained in past values of X 2 alone. Its mathematical formulation is based on linear regression modeling of stochastic processes.

Financial Development: Financial development thus involves the establishment and expansion of institutions, instruments and markets that support this investment and growth process.

Financial Depth: Financial depth captures the financial sector relative to the economy. It is the size of banks, other financial institutions, and financial markets in a country, taken together and compared to a measure of economic output.

Economic Growth: Economic growth is the increase in the inflation-adjusted market value of the goods and services produced by an economy over time. It is conventionally measured as the percent rate of increase in real gross domestic product, or real GDP.

Dynamic Panel Data Model: The ability of first differencing to remove unobserved heterogeneity also underlies the family of estimators that have been developed for dynamic panel data (DPD) models. These models contain one or more lagged dependent variables, allowing for the modeling of a partial adjustment mechanism.

Panel Time Series Methodology: In statistics and econometrics, the term panel data refers to multi-dimensional data frequently involving measurements over time. Panel data contain observations of multiple phenomena obtained over multiple time periods for the same firms or individuals. In biostatistics, the term longitudinal data is often used instead, wherein a subject or cluster constitutes a panel member or individual in a longitudinal study. Time series and cross-sectional data can be thought of as special cases of panel data that are in one dimension only